The overall goal of this project is to develop, evaluate and commercialize a computer-aided-diagnosis (CAD) system that supports an enhanced decision model for interpreting breast MR images. The proposed system will enable clinicians to more readily incorporate breast MR into their decision-making and patient care. The specific aim is to develop an integrated methodology to improve sensitivity rates of radiologists with minimal clinical experience in breast MR to levels that approximate sensitivity rates of radiologists with extensive experience in breast MR. The integrated methodology has three components: 1) Enhance the interpretation model previously proposed by Nunes, Schanll, et al. to contain additional decision nodes based on lesion density and kinetics. 2) Provide readers with a CAD system that displays quantitative and visualization aids to assist in the interpretation at the decision nodes. 3) Generate training tools and case studies for teaching radiologists how to interpret and identify lesion characteristics. The CAD system is based on the statistical surface fractal features and statistical border fractal features developed in prior research. The CAD system will be developed and tested using MR image data from a consortium of institutions that use different protocols and MR systems. A reader study will evaluate changes in interpretive and diagnostic performance of radiologists who have minimal clinical experience interpreting breast MR when they are provided with CAD support.